Distributed Finite Memory Estimation from Relative Measurements for Multiple-Robot Localization in Wireless Sensor Networks

Yeong Jun Kim, Hyun Ho Kang, Sang Su Lee, Jung Min Pak, Choon Ki Ahn

Research output: Contribution to journalArticlepeer-review

Abstract

Mobile robot localizations have been extensively studied, and various algorithms for multiple-robot localization have been developed. However, existing methods for multiple-robot localization often exhibit poor performance under harsh conditions, such as missing measurements and sudden appearance of obstacles. To overcome this problem, this paper proposes a novel method for multiple-robot localization in wireless sensor networks. The proposed method is theoretically based on the finite memory estimation and utilizes relative distance and angle measurements between robots. Thus, the proposed method is referred to as distributed finite memory estimation from relative measurements (DFMERM). Due to the finite memory structure, the DFMERM has inherent robustness against computational and modeling errors. Moreover, the novel distributed localization method using relative measurements shows the robustness against missing measurements. Robust DFMERM localization performance is experimentally demonstrated using multiple mobile robots under the harsh conditions.

Original languageEnglish
Pages (from-to)5980-5989
Number of pages10
JournalIEEE Access
Volume10
DOIs
Publication statusPublished - 2022

Keywords

  • Distributed localization
  • Finite memory estimation
  • Mobile robot
  • Relative measurements
  • Wireless sensor networks

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)
  • Electrical and Electronic Engineering

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